@inproceedings{
  author = {L. Zhu and C. Bockelmann and A. Dekorsy},
  year = {2024},
  month = {Dec},
  title = {SINR Sequence Compression and Quantization with VQ-VAE Method},
  URL = {https://globecom2024.ieee-globecom.org/},
  abstract={In this study, we introduce an innovative signal to interference and noise ratio (SINR) time sequence feedback scheme based on vector quantization variational autoencoder (VQ-VAE). We compress the SINR sequence at the user equipment (UE) side and reconstruct it at the base station (BS) side. The reconstructed sequence is then utilized for SINR prediction at the BS. The VQ-VAE framework compresses SINR sequences into a compact embedding space involving several embedding vectors. Instead of transmitting the entire compressed SINR sequence back, we only need to transmit the index of the corresponding embedded vector. Based on the index, the sequence will be reconstructed. Moreover, a principal component analysis (PCA) based method is employed to reshape the distribution of the embedding space and compression performance is improved consequently. Our numerical simulations demonstrate that VQ-VAE combined with PCA achieves superior reconstruction and prediction accuracy while requiring fewer quantization bits compared to 3GPP commonly used method, differential quantization. Therefore, the proposed scheme is a promising solution for enhancing SINR sequence compression and prediction in wireless communication systems.},
  booktitle={2024 IEEE Global Communications Conference (GLOBECOM) }
}